{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys \n",
    "\n",
    "# Modify the path \n",
    "sys.path.append(\"..\")\n",
    "\n",
    "import pandas as pd\n",
    "import yellowbrick as yb\n",
    "import matplotlib.pyplot as plt \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from yellowbrick.base import Visualizer\n",
    "from yellowbrick.exceptions import YellowbrickValueError\n",
    "\n",
    "from matplotlib import patches\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "##########################################################################\n",
    "## Legend Drawing Utilities\n",
    "##########################################################################\n",
    "\n",
    "def manual_legend(g, labels, colors, **legend_kwargs):\n",
    "    \"\"\"\n",
    "    Adds a manual legend for a scatter plot to the visualizer where the labels\n",
    "    and associated colors are drawn with circle patches instead of determining\n",
    "    them from the labels of the artist objects on the axes. This helper is\n",
    "    used either when there are a lot of duplicate labels, no labeled artists,\n",
    "    or when the color of the legend doesn't exactly match the color in the\n",
    "    figure (e.g. because of the use of transparency).\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    g : Visualizer or Axes object\n",
    "        The graph to draw the legend on, either a Visualizer or a matplotlib\n",
    "        Axes object. If None, the current axes are drawn on, but this is not\n",
    "        recommended.\n",
    "\n",
    "    labels : list of str\n",
    "        The text labels to associate with the legend. Note that the labels\n",
    "        will be added to the legend in the order specified.\n",
    "\n",
    "    colors : list of colors\n",
    "        A list of any valid matplotlib color reference. The number of colors\n",
    "        specified must be equal to the number of labels.\n",
    "\n",
    "    legend_kwargs : dict\n",
    "        Any additional keyword arguments to pass to the legend.\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    legend: Legend artist\n",
    "        The artist created by the ax.legend() call, returned for further\n",
    "        manipulation if required by the caller.\n",
    "\n",
    "    Notes\n",
    "    -----\n",
    "    Right now this method simply draws the patches as rectangles and cannot\n",
    "    take into account the line or scatter plot properties (e.g. line style or\n",
    "    marker style). It is possible to add Line2D patches to the artist that do\n",
    "    add manual styles like this, which we can explore in the future.\n",
    "\n",
    "    .. seealso:: https://matplotlib.org/gallery/text_labels_and_annotations/custom_legends.html\n",
    "    \"\"\"\n",
    "    # Get access to the matplotlib Axes\n",
    "    if isinstance(g, Visualizer):\n",
    "        g = g.ax\n",
    "    elif g is None:\n",
    "        g = plt.gca()\n",
    "\n",
    "    # Ensure that labels and colors are the same length to prevent odd behavior.\n",
    "    if len(colors) != len(labels):\n",
    "        raise YellowbrickValueError(\n",
    "            \"please specify the same number of colors as labels!\"\n",
    "        )\n",
    "\n",
    "    # Create the legend handles with the associated colors and labels\n",
    "    handles = [\n",
    "        patches.Patch(color=color, label=label)\n",
    "        for color, label in zip(colors, labels)\n",
    "    ]\n",
    "\n",
    "    # Return the Legend artist\n",
    "    return g.legend(handles=handles, **legend_kwargs)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# yellowbrick.text.tsne\n",
    "# Implements TSNE visualizations of documents in 2D space.\n",
    "#\n",
    "# Author:   Benjamin Bengfort <benjamin@bengfort.com>\n",
    "# Author:   Rebecca Bilbro <bilbro@gmail.com>\n",
    "# Created:  Mon Feb 20 06:33:29 2017 -0500\n",
    "#\n",
    "# Copyright (C) 2016 Bengfort.com\n",
    "# For license information, see LICENSE.txt\n",
    "#\n",
    "# ID: tsne.py [6aa9198] benjamin@bengfort.com $\n",
    "\n",
    "\"\"\"\n",
    "Implements TSNE visualizations of documents in 2D space.\n",
    "\"\"\"\n",
    "\n",
    "##########################################################################\n",
    "## Imports\n",
    "##########################################################################\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "from collections import defaultdict\n",
    "\n",
    "from yellowbrick.text.base import TextVisualizer\n",
    "from yellowbrick.style.colors import resolve_colors\n",
    "from yellowbrick.exceptions import YellowbrickValueError\n",
    "\n",
    "from sklearn.manifold import TSNE\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.decomposition import TruncatedSVD, PCA\n",
    "\n",
    "##########################################################################\n",
    "## Quick Methods\n",
    "##########################################################################\n",
    "\n",
    "def tsne(X, y=None, ax=None, decompose='svd', decompose_by=50, classes=None,\n",
    "           colors=None, colormap=None, alpha=0.7, **kwargs):\n",
    "    \"\"\"\n",
    "    Display a projection of a vectorized corpus in two dimensions using TSNE,\n",
    "    a nonlinear dimensionality reduction method that is particularly well\n",
    "    suited to embedding in two or three dimensions for visualization as a\n",
    "    scatter plot. TSNE is widely used in text analysis to show clusters or\n",
    "    groups of documents or utterances and their relative proximities.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "\n",
    "    X : ndarray or DataFrame of shape n x m\n",
    "        A matrix of n instances with m features representing the corpus of\n",
    "        vectorized documents to visualize with tsne.\n",
    "\n",
    "    y : ndarray or Series of length n\n",
    "        An optional array or series of target or class values for instances.\n",
    "        If this is specified, then the points will be colored according to\n",
    "        their class. Often cluster labels are passed in to color the documents\n",
    "        in cluster space, so this method is used both for classification and\n",
    "        clustering methods.\n",
    "\n",
    "    ax : matplotlib axes\n",
    "        The axes to plot the figure on.\n",
    "\n",
    "    decompose : string or None\n",
    "        A preliminary decomposition is often used prior to TSNE to make the\n",
    "        projection faster. Specify `\"svd\"` for sparse data or `\"pca\"` for\n",
    "        dense data. If decompose is None, the original data set will be used.\n",
    "\n",
    "    decompose_by : int\n",
    "        Specify the number of components for preliminary decomposition, by\n",
    "        default this is 50; the more components, the slower TSNE will be.\n",
    "\n",
    "    classes : list of strings\n",
    "        The names of the classes in the target, used to create a legend.\n",
    "\n",
    "    colors : list or tuple of colors\n",
    "        Specify the colors for each individual class\n",
    "\n",
    "    colormap : string or matplotlib cmap\n",
    "        Sequential colormap for continuous target\n",
    "\n",
    "    alpha : float, default: 0.7\n",
    "        Specify a transparency where 1 is completely opaque and 0 is completely\n",
    "        transparent. This property makes densely clustered points more visible.\n",
    "\n",
    "    kwargs : dict\n",
    "        Pass any additional keyword arguments to the TSNE transformer.\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    ax : matplotlib axes\n",
    "        Returns the axes that the parallel coordinates were drawn on.\n",
    "    \"\"\"\n",
    "    # Instantiate the visualizer\n",
    "    visualizer = TSNEVisualizer(\n",
    "        ax, decompose, decompose_by, classes, colors, colormap, alpha, **kwargs\n",
    "    )\n",
    "\n",
    "    # Fit and transform the visualizer (calls draw)\n",
    "    visualizer.fit(X, y, **kwargs)\n",
    "    visualizer.transform(X)\n",
    "\n",
    "    # Return the axes object on the visualizer\n",
    "    return visualizer.ax\n",
    "\n",
    "\n",
    "##########################################################################\n",
    "## TSNEVisualizer\n",
    "##########################################################################\n",
    "\n",
    "class TSNEVisualizer(TextVisualizer):\n",
    "    \"\"\"\n",
    "    Display a projection of a vectorized corpus in two dimensions using TSNE,\n",
    "    a nonlinear dimensionality reduction method that is particularly well\n",
    "    suited to embedding in two or three dimensions for visualization as a\n",
    "    scatter plot. TSNE is widely used in text analysis to show clusters or\n",
    "    groups of documents or utterances and their relative proximities.\n",
    "\n",
    "    TSNE will return a scatter plot of the vectorized corpus, such that each\n",
    "    point represents a document or utterance. The distance between two points\n",
    "    in the visual space is embedded using the probability distribution of\n",
    "    pairwise similarities in the higher dimensionality; thus TSNE shows\n",
    "    clusters of similar documents and the relationships between groups of\n",
    "    documents as a scatter plot.\n",
    "\n",
    "    TSNE can be used with either clustering or classification; by specifying\n",
    "    the ``classes`` argument, points will be colored based on their similar\n",
    "    traits. For example, by passing ``cluster.labels_`` as ``y`` in ``fit()``, all\n",
    "    points in the same cluster will be grouped together. This extends the\n",
    "    neighbor embedding with more information about similarity, and can allow\n",
    "    better interpretation of both clusters and classes.\n",
    "\n",
    "    For more, see https://lvdmaaten.github.io/tsne/\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "\n",
    "    ax : matplotlib axes\n",
    "        The axes to plot the figure on.\n",
    "\n",
    "    decompose : string or None, default: ``'svd'``\n",
    "        A preliminary decomposition is often used prior to TSNE to make the\n",
    "        projection faster. Specify ``\"svd\"`` for sparse data or ``\"pca\"`` for\n",
    "        dense data. If None, the original data set will be used.\n",
    "\n",
    "    decompose_by : int, default: 50\n",
    "        Specify the number of components for preliminary decomposition, by\n",
    "        default this is 50; the more components, the slower TSNE will be.\n",
    "\n",
    "    labels : list of strings\n",
    "        The names of the classes in the target, used to create a legend.\n",
    "        Labels must match names of classes in sorted order.\n",
    "\n",
    "    colors : list or tuple of colors\n",
    "        Specify the colors for each individual class\n",
    "\n",
    "    colormap : string or matplotlib cmap\n",
    "        Sequential colormap for continuous target\n",
    "\n",
    "    random_state : int, RandomState instance or None, optional, default: None\n",
    "        If int, random_state is the seed used by the random number generator;\n",
    "        If RandomState instance, random_state is the random number generator;\n",
    "        If None, the random number generator is the RandomState instance used\n",
    "        by np.random. The random state is applied to the preliminary\n",
    "        decomposition as well as tSNE.\n",
    "\n",
    "    alpha : float, default: 0.7\n",
    "        Specify a transparency where 1 is completely opaque and 0 is completely\n",
    "        transparent. This property makes densely clustered points more visible.\n",
    "\n",
    "    kwargs : dict\n",
    "        Pass any additional keyword arguments to the TSNE transformer.\n",
    "    \"\"\"\n",
    "\n",
    "    # NOTE: cannot be np.nan\n",
    "    NULL_CLASS = None\n",
    "\n",
    "    def __init__(self, ax=None, decompose='svd', decompose_by=50,\n",
    "                 labels=None, classes=None, colors=None, colormap=None,\n",
    "                 random_state=None, alpha=0.7, **kwargs):\n",
    "\n",
    "        # Visual Parameters\n",
    "        self.alpha = alpha\n",
    "        self.labels = labels\n",
    "        self.colors = colors\n",
    "        self.colormap = colormap\n",
    "        self.random_state = random_state\n",
    "\n",
    "        # Fetch TSNE kwargs from kwargs by popping only keys belonging to TSNE params\n",
    "        tsne_kwargs = {\n",
    "            key: kwargs.pop(key)\n",
    "            for key in TSNE().get_params()\n",
    "            if key in kwargs\n",
    "        }\n",
    "        self.transformer_ = self.make_transformer(decompose, decompose_by, tsne_kwargs)\n",
    "\n",
    "        # Call super at the end so that size and title are set correctly\n",
    "        super(TSNEVisualizer, self).__init__(ax=ax, **kwargs)\n",
    "\n",
    "    def make_transformer(self, decompose='svd', decompose_by=50, tsne_kwargs={}):\n",
    "        \"\"\"\n",
    "        Creates an internal transformer pipeline to project the data set into\n",
    "        2D space using TSNE, applying an pre-decomposition technique ahead of\n",
    "        embedding if necessary. This method will reset the transformer on the\n",
    "        class, and can be used to explore different decompositions.\n",
    "\n",
    "        Parameters\n",
    "        ----------\n",
    "\n",
    "        decompose : string or None, default: ``'svd'``\n",
    "            A preliminary decomposition is often used prior to TSNE to make\n",
    "            the projection faster. Specify ``\"svd\"`` for sparse data or ``\"pca\"``\n",
    "            for dense data. If decompose is None, the original data set will\n",
    "            be used.\n",
    "\n",
    "        decompose_by : int, default: 50\n",
    "            Specify the number of components for preliminary decomposition, by\n",
    "            default this is 50; the more components, the slower TSNE will be.\n",
    "\n",
    "        Returns\n",
    "        -------\n",
    "\n",
    "        transformer : Pipeline\n",
    "            Pipelined transformer for TSNE projections\n",
    "        \"\"\"\n",
    "\n",
    "        # TODO: detect decompose by inferring from sparse matrix or dense or\n",
    "        # If number of features > 50 etc.\n",
    "        decompositions = {\n",
    "            'svd': TruncatedSVD,\n",
    "            'pca': PCA,\n",
    "        }\n",
    "\n",
    "        if decompose and decompose.lower() not in decompositions:\n",
    "            raise YellowbrickValueError(\n",
    "                \"'{}' is not a valid decomposition, use {}, or None\".format(\n",
    "                    decompose, \", \".join(decompositions.keys())\n",
    "                )\n",
    "            )\n",
    "\n",
    "        # Create the pipeline steps\n",
    "        steps = []\n",
    "\n",
    "        # Add the pre-decomposition\n",
    "        if decompose:\n",
    "            klass = decompositions[decompose]\n",
    "            steps.append((decompose, klass(\n",
    "                n_components=decompose_by, random_state=self.random_state)))\n",
    "\n",
    "        # Add the TSNE manifold\n",
    "        steps.append(('tsne', TSNE(\n",
    "            n_components=2, random_state=self.random_state, **tsne_kwargs)))\n",
    "\n",
    "        # return the pipeline\n",
    "        return Pipeline(steps)\n",
    "\n",
    "    def fit(self, X, y=None, **kwargs):\n",
    "        \"\"\"\n",
    "        The fit method is the primary drawing input for the TSNE projection\n",
    "        since the visualization requires both X and an optional y value. The\n",
    "        fit method expects an array of numeric vectors, so text documents must\n",
    "        be vectorized before passing them to this method.\n",
    "\n",
    "        Parameters\n",
    "        ----------\n",
    "        X : ndarray or DataFrame of shape n x m\n",
    "            A matrix of n instances with m features representing the corpus of\n",
    "            vectorized documents to visualize with tsne.\n",
    "\n",
    "        y : ndarray or Series of length n\n",
    "            An optional array or series of target or class values for\n",
    "            instances. If this is specified, then the points will be colored\n",
    "            according to their class. Often cluster labels are passed in to\n",
    "            color the documents in cluster space, so this method is used both\n",
    "            for classification and clustering methods.\n",
    "\n",
    "        kwargs : dict\n",
    "            Pass generic arguments to the drawing method\n",
    "\n",
    "        Returns\n",
    "        -------\n",
    "        self : instance\n",
    "            Returns the instance of the transformer/visualizer\n",
    "        \"\"\"\n",
    "\n",
    "        # Store the classes we observed in y\n",
    "        if y is not None:\n",
    "            self.classes_ = np.unique(y)\n",
    "        elif y is None and self.labels is not None:\n",
    "            self.classes_ = np.array([self.labels[0]])\n",
    "        else:\n",
    "            self.classes_ = np.array([self.NULL_CLASS])\n",
    "\n",
    "        # Fit our internal transformer and transform the data.\n",
    "        vecs = self.transformer_.fit_transform(X)\n",
    "        self.n_instances_ = vecs.shape[0]\n",
    "\n",
    "        # Draw the vectors\n",
    "        self.draw(vecs, y, **kwargs)\n",
    "\n",
    "        # Fit always returns self.\n",
    "        return self\n",
    "\n",
    "    def draw(self, points, target=None, **kwargs):\n",
    "        \"\"\"\n",
    "        Called from the fit method, this method draws the TSNE scatter plot,\n",
    "        from a set of decomposed points in 2 dimensions. This method also\n",
    "        accepts a third dimension, target, which is used to specify the colors\n",
    "        of each of the points. If the target is not specified, then the points\n",
    "        are plotted as a single cloud to show similar documents.\n",
    "        \"\"\"\n",
    "        # Resolve the labels with the classes\n",
    "        labels = self.labels if self.labels is not None else self.classes_\n",
    "        if len(labels) != len(self.classes_):\n",
    "            raise YellowbrickValueError((\n",
    "                \"number of supplied labels ({}) does not \"\n",
    "                \"match the number of classes ({})\"\n",
    "            ).format(len(labels), len(self.classes_)))\n",
    "\n",
    "\n",
    "        # Create the color mapping for the labels.\n",
    "        self.color_values = resolve_colors(\n",
    "            n_colors=len(labels), colormap=self.colormap, colors=self.color)\n",
    "        colors = dict(zip(labels, self.color_values))\n",
    "\n",
    "        # Transform labels into a map of class to label\n",
    "        labels = dict(zip(self.classes_, labels))\n",
    "\n",
    "        # Expand the points into vectors of x and y for scatter plotting,\n",
    "        # assigning them to their label if the label has been passed in.\n",
    "        # Additionally, filter classes not specified directly by the user.\n",
    "        series = defaultdict(lambda: {'x':[], 'y':[]})\n",
    "\n",
    "        if target is not None:\n",
    "            for t, point in zip(target, points):\n",
    "                label = labels[t]\n",
    "                series[label]['x'].append(point[0])\n",
    "                series[label]['y'].append(point[1])\n",
    "        else:\n",
    "            label = self.classes_[0]\n",
    "            for x,y in points:\n",
    "                series[label]['x'].append(x)\n",
    "                series[label]['y'].append(y)\n",
    "\n",
    "        # Plot the points\n",
    "        for label, points in series.items():\n",
    "            self.ax.scatter(\n",
    "                points['x'], points['y'], c=colors[label],\n",
    "                alpha=self.alpha, label=label\n",
    "            )\n",
    "\n",
    "    def finalize(self, **kwargs):\n",
    "        \"\"\"\n",
    "        Finalize the drawing by adding a title and legend, and removing the\n",
    "        axes objects that do not convey information about TNSE.\n",
    "        \"\"\"\n",
    "        self.set_title(\n",
    "            \"TSNE Projection of {} Documents\".format(self.n_instances_)\n",
    "        )\n",
    "\n",
    "        # Remove the ticks\n",
    "        self.ax.set_yticks([])\n",
    "        self.ax.set_xticks([])\n",
    "            \n",
    "        # Add the legend outside of the figure box.\n",
    "        if not all(self.classes_ == np.array([self.NULL_CLASS])):\n",
    "            box = self.ax.get_position()\n",
    "            self.ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])\n",
    "            manual_legend(\n",
    "                self, self.classes_, self.color_values, \n",
    "                loc='center left', bbox_to_anchor=(1, 0.5)\n",
    "            )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from download import download_all \n",
    "from sklearn.datasets.base import Bunch\n",
    "\n",
    "## The path to the test data sets\n",
    "FIXTURES  = os.path.join(os.getcwd(), \"data\")\n",
    "\n",
    "## Dataset loading mechanisms\n",
    "datasets = {\n",
    "    \"hobbies\": os.path.join(FIXTURES, \"hobbies\")\n",
    "}\n",
    "\n",
    "\n",
    "def load_data(name, download=True):\n",
    "    \"\"\"\n",
    "    Loads and wrangles the passed in text corpus by name.\n",
    "    If download is specified, this method will download any missing files. \n",
    "    \"\"\"\n",
    "    \n",
    "    # Get the path from the datasets \n",
    "    path = datasets[name]\n",
    "    \n",
    "    # Check if the data exists, otherwise download or raise \n",
    "    if not os.path.exists(path):\n",
    "        if download:\n",
    "            download_all() \n",
    "        else:\n",
    "            raise ValueError((\n",
    "                \"'{}' dataset has not been downloaded, \"\n",
    "                \"use the download.py module to fetch datasets\"\n",
    "            ).format(name))\n",
    "    \n",
    "    # Read the directories in the directory as the categories. \n",
    "    categories = [\n",
    "        cat for cat in os.listdir(path) \n",
    "        if os.path.isdir(os.path.join(path, cat))\n",
    "    ]\n",
    "    \n",
    "    \n",
    "    files  = [] # holds the file names relative to the root \n",
    "    data   = [] # holds the text read from the file \n",
    "    target = [] # holds the string of the category \n",
    "        \n",
    "    # Load the data from the files in the corpus \n",
    "    for cat in categories:\n",
    "        for name in os.listdir(os.path.join(path, cat)):\n",
    "            files.append(os.path.join(path, cat, name))\n",
    "            target.append(cat)\n",
    "            \n",
    "            with open(os.path.join(path, cat, name), 'r') as f:\n",
    "                data.append(f.read())\n",
    "        \n",
    "    \n",
    "    # Return the data bunch for use similar to the newsgroups example\n",
    "    return Bunch(\n",
    "        categories=categories,\n",
    "        files=files,\n",
    "        data=data,\n",
    "        target=target,\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\n",
    "corpus = load_data('hobbies')\n",
    "tfidf  = TfidfVectorizer()\n",
    "\n",
    "docs   = tfidf.fit_transform(corpus.data)\n",
    "labels = corpus.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0.00784313725490196, 0.4470588235294118, 0.6352941176470588)\n",
      "(0.6235294117647059, 0.7647058823529411, 0.4666666666666667)\n",
      "(0.792156862745098, 0.043137254901960784, 0.011764705882352941)\n",
      "(0.6470588235294118, 0.00784313725490196, 0.34509803921568627)\n",
      "(0.8431372549019608, 0.7803921568627451, 0.011764705882352941)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tsne = TSNEVisualizer()\n",
    "tsne.fit(docs, labels)\n",
    "tsne.poof()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0.00784313725490196, 0.4470588235294118, 0.6352941176470588)\n",
      "(0.6235294117647059, 0.7647058823529411, 0.4666666666666667)\n",
      "(0.792156862745098, 0.043137254901960784, 0.011764705882352941)\n",
      "(0.6470588235294118, 0.00784313725490196, 0.34509803921568627)\n",
      "(0.8431372549019608, 0.7803921568627451, 0.011764705882352941)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tsne = TSNEVisualizer(alpha=0.5)\n",
    "tsne.fit(docs, labels)\n",
    "tsne.poof()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
